huge refactor for high RewritePatterns usage and less ad-hoc cpp code

remove Spatial many ops in favor of tensor ops like in pim

src/PIM/Conversion/ONNXToSpatial/CMakeLists.txt

@@ -3,6 +3,11 @@ mlir_tablegen(ONNXToSpatial.hpp.inc -gen-rewriters "-I${ONNX_MLIR_SRC_ROOT}")
 add_public_tablegen_target(ONNXToSpatialIncGen)
 
 add_pim_library(OMONNXToSpatial
+  ConversionPatterns.cpp
+  HostFoldability.cpp
+  HostLegality.cpp
+  PrePatterns.cpp
+  PostPatterns.cpp
   Patterns/Math/Conv.cpp
   Patterns/Math/Elementwise.cpp
   Patterns/Math/Gemm.cpp

src/PIM/Conversion/ONNXToSpatial/Common/Common.hpp

@@ -1,8 +1,7 @@
 #pragma once
 
-#include "src/Accelerators/PIM/Common/PimCommon.hpp"
-#include "src/Dialect/ONNX/ONNXOps.hpp"
-
 #include "ComputeRegionBuilder.hpp"
 #include "ShapeTilingUtils.hpp"
 #include "WeightMaterialization.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
+#include "src/Dialect/ONNX/ONNXOps.hpp"

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.cpp

@@ -5,6 +5,8 @@
 
 #include "ShapeTilingUtils.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 
 using namespace mlir;
 
@@ -30,10 +32,29 @@ SmallVector<Value> sliceTensor(
 
   for (int64_t i = 0; i < numSlices; i++) {
     offsets[axis] = rewriter.getIndexAttr(i * sliceSize);
-    if (i == numSlices - 1 && lastSliceSize != 0)
+    int64_t currentSliceSize = sliceSize;
+    if (i == numSlices - 1 && lastSliceSize != 0) {
+      currentSliceSize = lastSliceSize;
       sizes[axis] = rewriter.getIndexAttr(lastSliceSize);
+    }
 
-    Value slice = tensor::ExtractSliceOp::create(rewriter, loc, tensorToSlice, offsets, sizes, strides);
+    SmallVector<int64_t> sliceShape(shape.begin(), shape.end());
+    sliceShape[axis] = currentSliceSize;
+    auto sliceType =
+      RankedTensorType::get(sliceShape, cast<RankedTensorType>(tensorToSlice.getType()).getElementType());
+
+    Value slice;
+    if (isHostFoldableValue(tensorToSlice)) {
+      slice = tensor::ExtractSliceOp::create(rewriter, loc, tensorToSlice, offsets, sizes, strides);
+    }
+    else {
+      auto sliceCompute =
+        createSpatCompute<1>(rewriter, loc, TypeRange {sliceType}, {}, ValueRange {tensorToSlice}, [&](Value input) {
+          Value computedSlice = tensor::ExtractSliceOp::create(rewriter, loc, input, offsets, sizes, strides);
+          spatial::SpatYieldOp::create(rewriter, loc, computedSlice);
+        });
+      slice = sliceCompute.getResult(0);
+    }
     slices.push_back(slice);
   }
 

src/PIM/Conversion/ONNXToSpatial/Common/ShapeTilingUtils.hpp

@@ -5,15 +5,15 @@
 #include "mlir/IR/Value.h"
 #include "mlir/Transforms/DialectConversion.h"
 
+#include "llvm/ADT/ArrayRef.h"
+#include "llvm/ADT/DenseMap.h"
+#include "llvm/ADT/SmallVector.h"
+
 #include <cassert>
 #include <cstddef>
 #include <type_traits>
 #include <utility>
 
-#include "llvm/ADT/DenseMap.h"
-#include "llvm/ADT/ArrayRef.h"
-#include "llvm/ADT/SmallVector.h"
-
 namespace onnx_mlir {
 
 template <class ShapedType>
@@ -105,7 +105,8 @@ inline auto getTensorShape(mlir::Value tensor) {
 inline bool haveSameStaticShape(mlir::Value lhs, mlir::Value rhs) {
   auto lhsType = mlir::dyn_cast<mlir::RankedTensorType>(lhs.getType());
   auto rhsType = mlir::dyn_cast<mlir::RankedTensorType>(rhs.getType());
-  return lhsType && rhsType && lhsType.hasStaticShape() && rhsType.hasStaticShape() && lhsType.getShape() == rhsType.getShape();
+  return lhsType && rhsType && lhsType.hasStaticShape() && rhsType.hasStaticShape()
+      && lhsType.getShape() == rhsType.getShape();
 }
 
 /// Slices a statically shaped tensor along one axis into contiguous pieces of

src/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.cpp

@@ -5,12 +5,12 @@
 #include "mlir/IR/Value.h"
 #include "mlir/Support/LogicalResult.h"
 
+#include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/SmallPtrSet.h"
 #include "llvm/ADT/SmallVector.h"
-#include "llvm/ADT/STLExtras.h"
 
-#include "WeightMaterialization.hpp"
 #include "ShapeTilingUtils.hpp"
+#include "WeightMaterialization.hpp"
 #include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -28,7 +28,7 @@ bool isWeightLikeComputeOperand(Value value) {
   while (auto* definingOp = value.getDefiningOp()) {
     if (!visited.insert(definingOp).second)
       return false;
-    if (hasWeightAlways(definingOp))
+    if (isa<arith::ConstantOp, ONNXConstantOp>(definingOp) || hasWeightAlways(definingOp))
       return true;
 
     if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(definingOp)) {

src/PIM/Conversion/ONNXToSpatial/ConversionPatterns.cpp

@@ -0,0 +1,32 @@
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatial.hpp.inc"
+
+} // namespace
+
+void populateConversionPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx) {
+  patterns.add<removeLRN>(ctx);
+
+  populateElementwisePatterns(patterns, ctx);
+  populateGemmPatterns(patterns, ctx);
+  populateConvPatterns(patterns, ctx);
+  populatePoolPatterns(patterns, ctx);
+  populateReduceMeanPatterns(patterns, ctx);
+  populateReluPatterns(patterns, ctx);
+  populateSigmoidPatterns(patterns, ctx);
+  populateSoftmaxPatterns(patterns, ctx);
+  populateConcatPatterns(patterns, ctx);
+  populateGatherPatterns(patterns, ctx);
+  populateResizePatterns(patterns, ctx);
+  populateReshapePatterns(patterns, ctx);
+  populateSplitPatterns(patterns, ctx);
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp

@@ -5,6 +5,8 @@
 
 namespace onnx_mlir {
 
+void populateConversionPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
+
 void populateConvPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
 
 void populateElementwisePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);

src/PIM/Conversion/ONNXToSpatial/HostFoldability.cpp

@@ -0,0 +1,75 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/BuiltinTypes.h"
+
+#include "llvm/ADT/SmallPtrSet.h"
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+static bool hasStaticUnitStrides(tensor::ExtractSliceOp extractSliceOp) {
+  return llvm::all_of(extractSliceOp.getStaticStrides(), [](int64_t stride) { return stride == 1; });
+}
+
+static bool isStaticTensorResult(Operation* op) {
+  return llvm::all_of(op->getResultTypes(), [](Type type) {
+    auto shapedType = dyn_cast<ShapedType>(type);
+    return shapedType && shapedType.hasStaticShape();
+  });
+}
+
+static bool isHostFoldableOpImpl(Operation* op, llvm::SmallPtrSetImpl<Operation*>& visited) {
+  if (!op || !visited.insert(op).second)
+    return false;
+
+  if (isa<arith::ConstantOp, ONNXConstantOp, ONNXNoneOp>(op))
+    return true;
+
+  if (!isStaticTensorResult(op))
+    return false;
+
+  if (auto transposeOp = dyn_cast<ONNXTransposeOp>(op))
+    return isHostFoldableValue(transposeOp.getData());
+
+  if (auto collapseShapeOp = dyn_cast<tensor::CollapseShapeOp>(op))
+    return isHostFoldableValue(collapseShapeOp.getSrc());
+
+  if (auto expandShapeOp = dyn_cast<tensor::ExpandShapeOp>(op))
+    return isHostFoldableValue(expandShapeOp.getSrc());
+
+  if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(op))
+    return hasStaticUnitStrides(extractSliceOp) && isHostFoldableValue(extractSliceOp.getSource());
+
+  if (auto extractRowsOp = dyn_cast<spatial::SpatExtractRowsOp>(op))
+    return isHostFoldableValue(extractRowsOp.getInput());
+
+  if (auto concatOp = dyn_cast<spatial::SpatConcatOp>(op))
+    return llvm::all_of(concatOp.getInputs(), isHostFoldableValue);
+
+  return false;
+}
+
+} // namespace
+
+bool isHostFoldableValue(Value value) {
+  auto* definingOp = value.getDefiningOp();
+  if (!definingOp)
+    return false;
+
+  llvm::SmallPtrSet<Operation*, 8> visited;
+  return isHostFoldableOpImpl(definingOp, visited);
+}
+
+bool isHostFoldableOp(Operation* op) {
+  llvm::SmallPtrSet<Operation*, 8> visited;
+  return isHostFoldableOpImpl(op, visited);
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp

@@ -0,0 +1,12 @@
+#pragma once
+
+#include "mlir/IR/Operation.h"
+#include "mlir/IR/Value.h"
+
+namespace onnx_mlir {
+
+bool isHostFoldableValue(mlir::Value value);
+
+bool isHostFoldableOp(mlir::Operation* op);
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/HostLegality.cpp

@@ -0,0 +1,29 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostLegality.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+LogicalResult verifyONNXToSpatialHostLegality(func::FuncOp funcOp) {
+  bool hasFailure = false;
+
+  for (Operation& op : funcOp.getFunctionBody().front()) {
+    if (isa<func::ReturnOp, spatial::SpatCompute, spatial::SpatComputeBatch>(&op))
+      continue;
+    if (isHostFoldableOp(&op))
+      continue;
+
+    op.emitOpError("non-foldable top-level runtime op remains after ONNX-to-Spatial; lower it inside spat.compute");
+    hasFailure = true;
+  }
+
+  return success(!hasFailure);
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/HostLegality.hpp

@@ -0,0 +1,10 @@
+#pragma once
+
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Support/LogicalResult.h"
+
+namespace onnx_mlir {
+
+mlir::LogicalResult verifyONNXToSpatialHostLegality(mlir::func::FuncOp funcOp);
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/ONNXToSpatialPass.cpp

@@ -8,21 +8,17 @@
 #include "mlir/Transforms/GreedyPatternRewriteDriver.h"
 #include "mlir/Transforms/Passes.h"
 
-#include "llvm/ADT/STLExtras.h"
 #include "llvm/ADT/SmallVector.h"
-#include "llvm/Support/Casting.h"
 #include "llvm/Support/Debug.h"
-#include "llvm/Support/raw_os_ostream.h"
-
-#include <fstream>
-#include <iterator>
-#include <utility>
 
 #include "Common/Common.hpp"
 #include "Common/PimCommon.hpp"
+#include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Compiler/PimCompilerOptions.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
-#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostLegality.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PostPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PrePatterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Compiler/CompilerOptions.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
@@ -33,8 +29,6 @@ namespace onnx_mlir {
 
 namespace {
 
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatial.hpp.inc"
-
 struct ONNXToSpatialPass : PassWrapper<ONNXToSpatialPass, OperationPass<ModuleOp>> {
   MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ONNXToSpatialPass)
   StringRef getArgument() const override { return "convert-onnx-to-spatial"; }
@@ -44,71 +38,64 @@ struct ONNXToSpatialPass : PassWrapper<ONNXToSpatialPass, OperationPass<ModuleOp
   ONNXToSpatialPass(const ONNXToSpatialPass& pass) {}
 
   void runOnOperation() override;
-
-private:
-  void annotateWeightsConstants(func::FuncOp funcOp) const;
-  LogicalResult encapsulateGlobalInstruction(func::FuncOp funcOp);
-  LogicalResult promoteConstantInputsToWeights(func::FuncOp funcOp);
-  void populateEmptyFunction(func::FuncOp funcOp);
 };
 
 } // namespace
 
-static void foldSingleLaneComputeBatches(func::FuncOp funcOp) {
+static void populateEmptyFunction(func::FuncOp funcOp) {
   IRRewriter rewriter(funcOp.getContext());
-  SmallVector<spatial::SpatComputeBatch> batchOps;
-  funcOp.walk([&](spatial::SpatComputeBatch batchOp) { batchOps.push_back(batchOp); });
+  IRMapping mapper;
+  SmallVector<spatial::SpatCompute> computes(funcOp.getOps<spatial::SpatCompute>());
+  if (!computes.empty())
+    return;
 
-  for (auto batchOp : batchOps) {
-    if (batchOp.getLaneCount() != 1)
-      continue;
+  auto returnOp = cast<func::ReturnOp>(funcOp.getFunctionBody().front().getTerminator());
+  rewriter.setInsertionPoint(returnOp);
 
-    auto loc = batchOp.getLoc();
-    rewriter.setInsertionPoint(batchOp);
-    auto computeOp =
-      spatial::SpatCompute::create(rewriter, loc, batchOp.getResultTypes(), batchOp.getWeights(), batchOp.getInputs());
-    computeOp.getProperties().setOperandSegmentSizes(
-      {static_cast<int>(batchOp.getWeights().size()), static_cast<int>(batchOp.getInputs().size())});
+  SmallVector<Type> sourceTypes;
+  SmallVector<Location> sourceLocs;
+  sourceTypes.reserve(funcOp.getNumArguments());
+  sourceLocs.reserve(funcOp.getNumArguments());
+  for (Value source : funcOp.getArguments()) {
+    sourceTypes.push_back(source.getType());
+    sourceLocs.push_back(source.getLoc());
+  }
 
-    Block& templateBlock = batchOp.getBody().front();
-    SmallVector<Type> blockArgTypes;
-    SmallVector<Location> blockArgLocs;
-    for (BlockArgument arg : templateBlock.getArguments()) {
-      blockArgTypes.push_back(arg.getType());
-      blockArgLocs.push_back(loc);
-    }
-    auto* newBlock =
-      rewriter.createBlock(&computeOp.getBody(), computeOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
+  auto newCompute = spatial::SpatCompute::create(
+    rewriter, returnOp.getLoc(), returnOp.getOperandTypes(), funcOp.getArguments(), {}, {});
+  auto* newBlock = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLocs);
+  for (auto [blockArg, computeArg] : llvm::zip(newBlock->getArguments(), newCompute.getOperands()))
+    mapper.map(computeArg, blockArg);
+  newCompute.getProperties().setOperandSegmentSizes({0, static_cast<int>(sourceTypes.size())});
 
-    IRMapping mapper;
-    for (auto [oldArg, newArg] : llvm::zip(templateBlock.getArguments(), newBlock->getArguments()))
-      mapper.map(oldArg, newArg);
-    rewriter.setInsertionPointToEnd(newBlock);
-    for (Operation& op : templateBlock)
+  rewriter.setInsertionPointToEnd(newBlock);
+  for (Operation& op : funcOp.getOps())
+    if (!isa<spatial::SpatCompute, func::ReturnOp>(&op))
       rewriter.clone(op, mapper);
 
-    batchOp.replaceAllUsesWith(computeOp.getResults());
-    rewriter.eraseOp(batchOp);
-  }
+  auto yield = spatial::SpatYieldOp::create(rewriter, funcOp.getLoc(), returnOp.getOperands());
+  for (size_t i = 0; i < yield.getNumOperands(); ++i)
+    yield.setOperand(i, mapper.lookupOrDefault(yield.getOperand(i)));
+
+  for (Operation& op : llvm::make_early_inc_range(funcOp.getOps()))
+    if (!isa<spatial::SpatCompute, func::ReturnOp>(&op)) {
+      op.dropAllUses();
+      rewriter.eraseOp(&op);
+    }
+
+  for (auto [index, computeResult] : llvm::enumerate(newCompute.getResults()))
+    returnOp.setOperand(index, computeResult);
 }
 
 void ONNXToSpatialPass::runOnOperation() {
   ModuleOp moduleOp = getOperation();
   MLIRContext* ctx = &getContext();
 
-  RewritePatternSet mergeActivationPatterns(ctx);
-  mergeActivationPatterns.add<onnxToArithConstant>(ctx);
-  mergeActivationPatterns.add<convAddToConvWithBiasLeft>(ctx);
-  mergeActivationPatterns.add<convAddToConvWithBiasRight>(ctx);
-  mergeActivationPatterns.add<matMulAddToGemm>(ctx);
-  mergeActivationPatterns.add<matMulToGemm>(ctx);
-  mergeActivationPatterns.add<removeFlattenSameShape>(ctx);
-  populateMatMulRewritePatterns(mergeActivationPatterns, ctx);
+  RewritePatternSet prePatterns(ctx);
+  populatePrePatterns(prePatterns, ctx);
+  if (failed(applyPatternsGreedily(moduleOp, std::move(prePatterns))))
+    llvm::dbgs() << "Failed to apply pre-patterns, continuing...\n";
 
-  if (failed(applyPatternsGreedily(moduleOp, std::move(mergeActivationPatterns))))
-    llvm::dbgs() << "Failed to merge activation patterns, continuing...\n";
-
-  IRRewriter rewriter(moduleOp);
   auto entryFunc = getPimEntryFunc(moduleOp);
   if (failed(entryFunc)) {
     signalPassFailure();
@@ -140,34 +127,23 @@ void ONNXToSpatialPass::runOnOperation() {
   target.addIllegalOp<ONNXReduceMeanV13Op>();
   target.addIllegalOp<ONNXSplitOp>();
 
-  RewritePatternSet patterns(ctx);
-  patterns.add<removeLRN>(ctx);
-
-  populateElementwisePatterns(patterns, ctx);
-  populateGemmPatterns(patterns, ctx);
-  populateConvPatterns(patterns, ctx);
-  populatePoolPatterns(patterns, ctx);
-  populateReduceMeanPatterns(patterns, ctx);
-  populateReluPatterns(patterns, ctx);
-  populateSigmoidPatterns(patterns, ctx);
-  populateSoftmaxPatterns(patterns, ctx);
-  populateConcatPatterns(patterns, ctx);
-  populateGatherPatterns(patterns, ctx);
-  populateResizePatterns(patterns, ctx);
-  populateReshapePatterns(patterns, ctx);
-  populateSplitPatterns(patterns, ctx);
-
-  if (failed(applyPartialConversion(moduleOp, target, std::move(patterns)))) {
+  RewritePatternSet conversionPatterns(ctx);
+  populateConversionPatterns(conversionPatterns, ctx);
+  if (failed(applyPartialConversion(moduleOp, target, std::move(conversionPatterns)))) {
     signalPassFailure();
     return;
   }
 
-  foldSingleLaneComputeBatches(*entryFunc);
+  RewritePatternSet earlyPostPatterns(ctx);
+  populateEarlyPostPatterns(earlyPostPatterns, ctx);
+  if (failed(applyPatternsGreedily(*entryFunc, std::move(earlyPostPatterns)))) {
+    signalPassFailure();
+    return;
+  }
 
-  // Count the number of compute ops and check they do not exceed the core count
   if (coresCount != -1) {
     int computeOpsCount = 0;
-    for (auto& op : entryFunc->getFunctionBody().front().getOperations())
+    for (Operation& op : entryFunc->getFunctionBody().front().getOperations())
       if (isa<spatial::SpatCompute>(op))
         computeOpsCount++;
 
@@ -185,355 +161,23 @@ void ONNXToSpatialPass::runOnOperation() {
 
   annotateWeightsConstants(*entryFunc);
 
+  RewritePatternSet postPatterns(ctx);
+  populatePostPatterns(postPatterns, ctx);
+  if (failed(applyPatternsGreedily(*entryFunc, std::move(postPatterns)))) {
+    signalPassFailure();
+    return;
+  }
+
+  if (failed(verifyONNXToSpatialHostLegality(*entryFunc))) {
+    signalPassFailure();
+    return;
+  }
+
   populateEmptyFunction(*entryFunc);
 
-  if (failed(encapsulateGlobalInstruction(*entryFunc))) {
-    signalPassFailure();
-    return;
-  }
-
-  if (failed(promoteConstantInputsToWeights(*entryFunc))) {
-    signalPassFailure();
-    return;
-  }
-
-  // Dump to file for debug
   dumpModule(moduleOp, "spatial0");
 }
 
-template <typename T>
-bool encapsulator(IRRewriter& rewriter, Location loc, Operation* inst, std::function<Value(T)> funcSource) {
-  if (T toRemoveOp = llvm::dyn_cast_if_present<T>(inst)) {
-    Value source = funcSource(toRemoveOp);
-    rewriter.setInsertionPointAfter(toRemoveOp);
-    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
-    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
-    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
-    rewriter.setInsertionPointToEnd(BB);
-    IRMapping mapper;
-    mapper.map(source, BB->getArgument(0));
-    auto newInst = rewriter.clone(*inst, mapper);
-    spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
-    inst->replaceAllUsesWith(newCompute->getResults());
-    inst->erase();
-    return true;
-  }
-  return false;
-}
-
-bool encapsulateSlice(IRRewriter& rewriter, Location loc, Operation* inst) {
-  if (tensor::ExtractSliceOp toRemoveOp = llvm::dyn_cast_if_present<tensor::ExtractSliceOp>(inst)) {
-    auto source = toRemoveOp.getSource();
-    rewriter.setInsertionPointAfter(toRemoveOp);
-    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), source);
-    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), {source.getType()}, {loc});
-    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) 1});
-    rewriter.setInsertionPointToEnd(BB);
-    IRMapping mapper;
-    mapper.map(source, BB->getArgument(0));
-    auto newInst = rewriter.clone(*inst, mapper);
-    spatial::SpatYieldOp::create(rewriter, loc, newInst->getResults());
-    inst->replaceAllUsesWith(newCompute->getResults());
-    inst->erase();
-    return true;
-  }
-  return false;
-}
-
-bool encapsulateConcat(IRRewriter& rewriter, Location loc, Operation* inst) {
-  if (auto toRemoveOp = llvm::dyn_cast_if_present<tensor::ConcatOp>(inst)) {
-    auto sources = toRemoveOp.getInputs();
-    rewriter.setInsertionPointAfter(toRemoveOp);
-    if (llvm::any_of(sources,
-                     [](auto source) { return isa_and_present<spatial::SpatCompute>(source.getDefiningOp()); })) {
-      auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), sources);
-      SmallVector<Type> sourceTypes;
-      SmallVector<Location> sourceLoc;
-      for (auto source : sources) {
-        sourceTypes.push_back(source.getType());
-        sourceLoc.push_back(loc);
-      }
-      auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLoc);
-      newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) sources.size()});
-      rewriter.setInsertionPointToEnd(BB);
-      IRMapping mapper;
-      for (auto [source, bbArg] : llvm::zip(sources, BB->getArguments()))
-        mapper.map(source, bbArg);
-      auto newConcat = spatial::SpatConcatOp::create(rewriter,
-                                                     loc,
-                                                     toRemoveOp.getType(),
-                                                     rewriter.getI64IntegerAttr(toRemoveOp.getDim()),
-                                                     ValueRange(BB->getArguments()));
-      spatial::SpatYieldOp::create(rewriter, loc, newConcat.getOutput());
-      inst->replaceAllUsesWith(newCompute->getResults());
-      inst->erase();
-      return true;
-    }
-    auto newCompute = spatial::SpatCompute::create(rewriter, loc, inst->getResultTypes(), sources);
-    SmallVector<Type> sourceTypes;
-    SmallVector<Location> sourceLoc;
-    for (auto source : sources) {
-      sourceTypes.push_back(source.getType());
-      sourceLoc.push_back(loc);
-    }
-    auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLoc);
-    newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) sources.size()});
-    rewriter.setInsertionPointToEnd(BB);
-    IRMapping mapper;
-    for (auto [source, bbArg] : llvm::zip(sources, BB->getArguments()))
-      mapper.map(source, bbArg);
-    auto newConcat = rewriter.clone(*inst, mapper);
-    spatial::SpatYieldOp::create(rewriter, loc, newConcat->getResults());
-    inst->replaceAllUsesWith(newCompute->getResults());
-    inst->erase();
-    return true;
-  }
-  return false;
-}
-
-static FailureOr<bool> sourceOperandHasWeightAlways(Operation* op) {
-  if (op == nullptr)
-    return false;
-
-  Operation* source = nullptr;
-  do {
-
-    if (isa<spatial::SpatCompute, spatial::SpatComputeBatch>(*op)) {
-      return false;
-    }
-    else if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(*op)) {
-      auto tmpSource = extractSliceOp.getSource();
-      auto definingOp = tmpSource.getDefiningOp();
-      if (definingOp)
-        op = definingOp;
-      else
-        return false;
-    }
-    else if (auto extractRowsOp = dyn_cast<spatial::SpatExtractRowsOp>(*op)) {
-      auto tmpSource = extractRowsOp.getInput();
-      auto definingOp = tmpSource.getDefiningOp();
-      if (definingOp)
-        op = definingOp;
-      else
-        return false;
-    }
-    else if (auto expandShapeOp = dyn_cast<tensor::ExpandShapeOp>(*op)) {
-      auto tmpSource = expandShapeOp.getSrc();
-      auto definingOp = tmpSource.getDefiningOp();
-      if (definingOp)
-        op = definingOp;
-      else
-        return false;
-    }
-    else if (auto transposeOp = dyn_cast<ONNXTransposeOp>(*op)) {
-      auto tmpSource = transposeOp.getData();
-      auto definingOp = tmpSource.getDefiningOp();
-      if (definingOp)
-        op = definingOp;
-      else
-        return false;
-    }
-    else if (auto collapseShapeOp = dyn_cast<tensor::CollapseShapeOp>(*op)) {
-      auto tmpSource = collapseShapeOp.getSrc();
-      auto definingOp = tmpSource.getDefiningOp();
-      if (definingOp)
-        op = definingOp;
-      else
-        return false;
-    }
-    else if (auto constantOp = dyn_cast<arith::ConstantOp>(*op)) {
-      source = constantOp;
-    }
-    else if (auto concatOp = dyn_cast<tensor::ConcatOp>(*op)) {
-      bool res = false;
-      for (auto operand : concatOp.getOperands()) {
-        res |= hasWeightAlways(operand.getDefiningOp());
-        if (res)
-          return res;
-      }
-      return res;
-    }
-    else if (auto concatOp = dyn_cast<spatial::SpatConcatOp>(*op)) {
-      bool res = false;
-      for (auto operand : concatOp.getOperands()) {
-        res |= hasWeightAlways(operand.getDefiningOp());
-        if (res)
-          return res;
-      }
-      return res;
-    }
-    else {
-      op->emitOpError("unsupported global instruction while promoting weight-backed operands into Spatial computes");
-      return failure();
-    }
-  }
-  while (source == nullptr);
-
-  return hasWeightAlways(source);
-}
-
-// TODO what we want to keep in global?
-LogicalResult ONNXToSpatialPass::encapsulateGlobalInstruction(func::FuncOp funcOp) {
-  Location loc = funcOp.getLoc();
-  IRRewriter rewriter(&getContext());
-  bool keep = true;
-  while (keep) {
-    keep = false;
-    for (auto& instruction : llvm::make_early_inc_range(funcOp.getOps())) {
-      if (isa<spatial::SpatCompute, spatial::SpatComputeBatch, spatial::SpatExtractRowsOp>(instruction)
-          || isa<func::ReturnOp>(instruction))
-        continue;
-
-      auto weightBacked = sourceOperandHasWeightAlways(&instruction);
-      if (failed(weightBacked))
-        return failure();
-      if (*weightBacked)
-        continue;
-
-      keep |= encapsulateSlice(rewriter, loc, &instruction);
-
-      keep |= encapsulator<tensor::ExpandShapeOp>(
-        rewriter, loc, &instruction, [](tensor::ExpandShapeOp expand) { return expand.getSrc(); });
-
-      keep |= encapsulator<ONNXTransposeOp>(
-        rewriter, loc, &instruction, [](ONNXTransposeOp transpose) { return transpose.getData(); });
-
-      keep |= encapsulator<tensor::CollapseShapeOp>(
-        rewriter, loc, &instruction, [](tensor::CollapseShapeOp collapse) { return collapse.getSrc(); });
-
-      keep |= encapsulateConcat(rewriter, loc, &instruction);
-    }
-  }
-  return success();
-}
-
-void ONNXToSpatialPass::annotateWeightsConstants(func::FuncOp funcOp) const {
-  funcOp.walk([&](arith::ConstantOp constantOp) {
-    if (hasOnlySpatialMvmVmmWeightUses(constantOp.getResult()))
-      markWeightAlways(constantOp);
-  });
-}
-
-LogicalResult ONNXToSpatialPass::promoteConstantInputsToWeights(func::FuncOp funcOp) {
-  IRRewriter rewriter(&getContext());
-  SmallVector<spatial::SpatCompute> computes(funcOp.getOps<spatial::SpatCompute>());
-
-  for (auto compute : computes) {
-    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
-    bool needsRewrite = false;
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (!isWeightLikeComputeOperand(input))
-        continue;
-      promoteInput[inputIdx] = true;
-      needsRewrite = true;
-    }
-    if (!needsRewrite)
-      continue;
-
-    rewriter.setInsertionPointAfter(compute);
-
-    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
-    SmallVector<Value> newInputs;
-    SmallVector<Type> newInputTypes;
-    SmallVector<Location> newInputLocs;
-    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
-    newInputs.reserve(compute.getInputs().size());
-    newInputTypes.reserve(compute.getInputs().size());
-    newInputLocs.reserve(compute.getInputs().size());
-
-    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
-      if (promoteInput[inputIdx]) {
-        newWeights.push_back(input);
-        continue;
-      }
-      newInputs.push_back(input);
-      newInputTypes.push_back(input.getType());
-      newInputLocs.push_back(input.getLoc());
-    }
-
-    auto newCompute =
-      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
-    auto* newBlock =
-      rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), newInputTypes, newInputLocs);
-    newCompute.getProperties().setOperandSegmentSizes(
-      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
-    rewriter.setInsertionPointToStart(newBlock);
-
-    IRMapping mapper;
-    auto& oldBlock = compute.getBody().front();
-    size_t newInputIdx = 0;
-    for (auto [oldInputIdx, oldArg] : llvm::enumerate(oldBlock.getArguments())) {
-      if (!promoteInput[oldInputIdx]) {
-        mapper.map(oldArg, newBlock->getArgument(newInputIdx++));
-        continue;
-      }
-
-      auto clonedValue = materializeWeightLikeValueInBlock(compute.getInputs()[oldInputIdx], rewriter, mapper);
-      if (failed(clonedValue))
-        return compute.emitError("failed to materialize promoted weight-like operand inside compute body");
-      mapper.map(oldArg, *clonedValue);
-    }
-
-    for (auto& op : oldBlock.without_terminator())
-      rewriter.clone(op, mapper);
-
-    auto oldYield = cast<spatial::SpatYieldOp>(oldBlock.getTerminator());
-    SmallVector<Value> newYieldOperands;
-    newYieldOperands.reserve(oldYield.getOutputs().size());
-    for (Value operand : oldYield.getOutputs()) {
-      auto mapped = mapper.lookupOrNull(operand);
-      newYieldOperands.push_back(mapped ? cast<Value>(mapped) : operand);
-    }
-    spatial::SpatYieldOp::create(rewriter, oldYield.getLoc(), newYieldOperands);
-
-    compute.replaceAllUsesWith(newCompute);
-    compute.erase();
-  }
-
-  return success();
-}
-
-void ONNXToSpatialPass::populateEmptyFunction(func::FuncOp funcOp) {
-  IRRewriter rewriter(&getContext());
-  IRMapping mapper;
-  SmallVector<spatial::SpatCompute> computes(funcOp.getOps<spatial::SpatCompute>());
-  if (!computes.empty())
-    return;
-  auto returnOp = llvm::cast<func::ReturnOp>(funcOp.getRegion().front().getTerminator());
-  rewriter.setInsertionPoint(returnOp);
-
-  SmallVector<Type> sourceTypes;
-  SmallVector<Location> sourceLoc;
-  for (auto source : funcOp.getArguments()) {
-    sourceTypes.push_back(source.getType());
-    sourceLoc.push_back(source.getLoc());
-  }
-
-  auto newCompute = spatial::SpatCompute::create(
-    rewriter, returnOp.getLoc(), returnOp.getOperandTypes(), funcOp.getArguments(), {}, {});
-  auto BB = rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), sourceTypes, sourceLoc);
-  for (auto [bbArg, computeArg] : llvm::zip(BB->getArguments(), newCompute.getOperands()))
-    mapper.map(computeArg, bbArg);
-  newCompute.getProperties().setOperandSegmentSizes({(int) 0, (int) sourceTypes.size()});
-  rewriter.setInsertionPointToEnd(BB);
-  for (Operation& inst : funcOp.getOps())
-    if (!isa<spatial::SpatCompute, func::ReturnOp>(&inst))
-      rewriter.clone(inst, mapper);
-
-  auto yield = spatial::SpatYieldOp::create(rewriter, funcOp.getLoc(), returnOp.getOperands());
-  for (size_t i = 0; i < yield.getNumOperands(); ++i)
-    yield.setOperand(i, mapper.lookupOrDefault(yield.getOperand(i)));
-
-  for (Operation& inst : llvm::make_early_inc_range(funcOp.getOps()))
-    if (!isa<spatial::SpatCompute, func::ReturnOp>(&inst)){
-      inst.dropAllUses();
-      rewriter.eraseOp(&inst);
-  }
-
-  for (auto [index, computeResult] : llvm::enumerate(newCompute.getResults()))
-    returnOp.setOperand(index, computeResult);
-}
-
 std::unique_ptr<Pass> createONNXToSpatialPass() { return std::make_unique<ONNXToSpatialPass>(); }
 
 } // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Elementwise.cpp

@@ -5,9 +5,9 @@
 
 #include "llvm/ADT/SmallVector.h"
 
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
 #include "src/Accelerators/PIM/Common/IR/ShapeUtils.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/Gemm.cpp

@@ -11,6 +11,7 @@
 #include "src/Accelerators/PIM/Common/PimCommon.hpp"
 #include "src/Accelerators/PIM/Common/Support/Diagnostics.hpp"
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -49,6 +50,45 @@ materializeScaledConstantTensor(Value value, float factor, ConversionPatternRewr
   return arith::ConstantOp::create(rewriter, loc, denseAttr.getType(), scaledAttr).getResult();
 }
 
+static Value transposeForSpatial(Value value,
+                                 RankedTensorType resultType,
+                                 ArrayRef<int64_t> permutation,
+                                 ConversionPatternRewriter& rewriter,
+                                 Location loc) {
+  if (isHostFoldableValue(value))
+    return ONNXTransposeOp::create(rewriter, loc, resultType, value, rewriter.getI64ArrayAttr(permutation));
+
+  auto computeOp = createSpatCompute<1>(rewriter, loc, TypeRange {resultType}, {}, value, [&](Value input) {
+    Value transposed = ONNXTransposeOp::create(rewriter, loc, resultType, input, rewriter.getI64ArrayAttr(permutation));
+    spatial::SpatYieldOp::create(rewriter, loc, transposed);
+  });
+  return computeOp.getResult(0);
+}
+
+static Value
+expandRankOneBias(Value value, RankedTensorType resultType, ConversionPatternRewriter& rewriter, Location loc) {
+  if (isHostFoldableValue(value))
+    return tensor::ExpandShapeOp::create(rewriter,
+                                         loc,
+                                         resultType,
+                                         value,
+                                         SmallVector<ReassociationIndices> {
+                                           {0, 1}
+    });
+
+  auto computeOp = createSpatCompute<1>(rewriter, loc, TypeRange {resultType}, {}, value, [&](Value input) {
+    Value expanded = tensor::ExpandShapeOp::create(rewriter,
+                                                   loc,
+                                                   resultType,
+                                                   input,
+                                                   SmallVector<ReassociationIndices> {
+                                                     {0, 1}
+    });
+    spatial::SpatYieldOp::create(rewriter, loc, expanded);
+  });
+  return computeOp.getResult(0);
+}
+
 struct GemmToManyGemv : OpConversionPattern<ONNXGemmOp> {
   using OpConversionPattern::OpConversionPattern;
 
@@ -81,6 +121,11 @@ static SmallVector<Value> materializeBatchRowSlices(Value matrix,
   auto rowType = RankedTensorType::get({1, matrixType.getDimSize(1)}, matrixType.getElementType());
   SmallVector<Type> resultTypes(static_cast<size_t>(numRows), rowType);
 
+  if (isHostFoldableValue(matrix)) {
+    auto extractRowsOp = spatial::SpatExtractRowsOp::create(rewriter, loc, TypeRange(resultTypes), matrix);
+    return SmallVector<Value>(extractRowsOp->result_begin(), extractRowsOp->result_end());
+  }
+
   auto buildRowSlices = [&](Value matrixArg) {
     auto extractRowsOp = spatial::SpatExtractRowsOp::create(rewriter, loc, TypeRange(resultTypes), matrixArg);
     return SmallVector<Value>(extractRowsOp->result_begin(), extractRowsOp->result_end());
@@ -122,7 +167,8 @@ static SmallVector<Value> materializeBatchRowSlices(Value matrix,
     rootValue = definingOp->getOperand(0);
   }
 
-  return buildRowSlices(matrix);
+  SmallVector<Operation*> reversedChainOps(chainOps.rbegin(), chainOps.rend());
+  return cloneBatchInputChainIntoSliceCompute(rootValue, reversedChainOps, rootValue);
 }
 
 } // namespace
@@ -175,13 +221,7 @@ LogicalResult GemmToManyGemv::matchAndRewrite(ONNXGemmOp gemmOp,
     // Expand rank-1 bias [N] to rank-2 [1, N] for uniform handling
     if (cType.getRank() == 1) {
       auto expandedType = RankedTensorType::get({1, cType.getDimSize(0)}, cType.getElementType());
-      c = tensor::ExpandShapeOp::create(rewriter,
-                                        loc,
-                                        expandedType,
-                                        c,
-                                        SmallVector<ReassociationIndices> {
-                                          {0, 1}
-      });
+      c = expandRankOneBias(c, expandedType, rewriter, loc);
       cType = expandedType;
     }
     if (!cType.hasStaticShape()) {
@@ -196,25 +236,18 @@ LogicalResult GemmToManyGemv::matchAndRewrite(ONNXGemmOp gemmOp,
   }
 
   auto outRowType = RankedTensorType::get({1, outType.getDimSize(1)}, outType.getElementType());
+  SmallVector<Value> aSlices = materializeBatchRowSlices(a, aType, rewriter, loc);
+  SmallVector<Value> cSlices;
+  if (hasC && cHasNumOutRows)
+    cSlices = materializeBatchRowSlices(c, cType, rewriter, loc);
 
   SmallVector<Value> gemvOps;
-  gemvOps.reserve(numOutRows);
+  gemvOps.reserve(static_cast<size_t>(numOutRows));
   for (int64_t rowIdx = 0; rowIdx < numOutRows; rowIdx++) {
-    SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(rowIdx), rewriter.getIndexAttr(0)};
-    SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(aType.getDimSize(1))};
-    SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-    auto aSliceType = RankedTensorType::get({1, aType.getDimSize(1)}, aType.getElementType());
-    auto aSlice = tensor::ExtractSliceOp::create(rewriter, loc, aSliceType, a, offsets, sizes, strides).getResult();
-
     Value cSlice = c;
     if (hasC) {
-      if (cHasNumOutRows) {
-        SmallVector<OpFoldResult> offsets = {rewriter.getIndexAttr(rowIdx), rewriter.getIndexAttr(0)};
-        SmallVector<OpFoldResult> sizes = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(cType.getDimSize(1))};
-        SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-        auto cSliceType = RankedTensorType::get({1, cType.getDimSize(1)}, cType.getElementType());
-        cSlice = tensor::ExtractSliceOp::create(rewriter, loc, cSliceType, c, offsets, sizes, strides).getResult();
-      }
+      if (cHasNumOutRows)
+        cSlice = cSlices[static_cast<size_t>(rowIdx)];
       else if (!isVectorShape(getTensorShape(c))) {
         gemmOp.emitOpError("requires Gemm bias C to be vector-like when shared across decomposed rows");
         return failure();
@@ -224,7 +257,7 @@ LogicalResult GemmToManyGemv::matchAndRewrite(ONNXGemmOp gemmOp,
     auto gemvOp = ONNXGemmOp::create(rewriter,
                                      loc,
                                      outRowType,
-                                     aSlice,
+                                     aSlices[static_cast<size_t>(rowIdx)],
                                      b,
                                      cSlice,
                                      rewriter.getF32FloatAttr(1.0f),
@@ -267,13 +300,7 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
     // Expand rank-1 bias [N] to rank-2 [1, N] for uniform handling
     if (cType.getRank() == 1) {
       auto expandedType = RankedTensorType::get({1, cType.getDimSize(0)}, cType.getElementType());
-      c = tensor::ExpandShapeOp::create(rewriter,
-                                        gemmLoc,
-                                        expandedType,
-                                        c,
-                                        SmallVector<ReassociationIndices> {
-                                          {0, 1}
-      });
+      c = expandRankOneBias(c, expandedType, rewriter, gemmLoc);
       cType = expandedType;
     }
     if (!cType.hasStaticShape()) {
@@ -305,13 +332,14 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
 
   if (transA) {
     auto aShape = aType.getShape();
-    auto transposedType = aType.cloneWith(ArrayRef({aShape[1], aShape[0]}), aType.getElementType());
-    a = ONNXTransposeOp::create(rewriter, gemmLoc, transposedType, a, rewriter.getI64ArrayAttr({1, 0}));
+    auto transposedType = RankedTensorType::get({aShape[1], aShape[0]}, aType.getElementType());
+    a = transposeForSpatial(a, transposedType, {1, 0}, rewriter, gemmLoc);
+    aType = cast<RankedTensorType>(a.getType());
   }
   if (transB) {
     auto bShape = bType.getShape();
-    auto transposedType = bType.cloneWith(ArrayRef({bShape[1], bShape[0]}), bType.getElementType());
-    b = ONNXTransposeOp::create(rewriter, gemmLoc, transposedType, b, rewriter.getI64ArrayAttr({1, 0}));
+    auto transposedType = RankedTensorType::get({bShape[1], bShape[0]}, bType.getElementType());
+    b = transposeForSpatial(b, transposedType, {1, 0}, rewriter, gemmLoc);
     bType = cast<RankedTensorType>(b.getType());
   }
 
@@ -335,7 +363,6 @@ LogicalResult GemvToSpatialCompute::matchAndRewrite(ONNXGemmOp gemmOp,
   auto [aNumHSlices, aLastHSliceSize] = ceilIntegerDivideWithRemainder(aType.getDimSize(1), crossbarSize.getValue());
   auto [bNumHSlices, bLastHSliceSize] = ceilIntegerDivideWithRemainder(bType.getDimSize(1), crossbarSize.getValue());
   auto bNumVSlices = aNumHSlices;
-  auto bLastVSliceSize = aLastHSliceSize;
   auto cNumHSlices = bNumHSlices;
   auto cLastHSliceSize = bLastHSliceSize;
   auto outNumHSlices = cNumHSlices;
@@ -469,12 +496,15 @@ LogicalResult GemmToSpatialComputeBatch::matchAndRewrite(ONNXGemmOp gemmOp,
 
   if (gemmOpAdaptor.getTransB()) {
     auto bShape = bType.getShape();
-    auto transposedType = bType.cloneWith(ArrayRef({bShape[1], bShape[0]}), bType.getElementType());
-    b = ONNXTransposeOp::create(rewriter, loc, transposedType, b, rewriter.getI64ArrayAttr({1, 0}));
+    auto transposedType = RankedTensorType::get({bShape[1], bShape[0]}, bType.getElementType());
+    b = transposeForSpatial(b, transposedType, {1, 0}, rewriter, loc);
     bType = cast<RankedTensorType>(b.getType());
   }
   (void) bType;
 
+  if (!isHostFoldableValue(b))
+    return failure();
+
   Value sharedBias;
   if (hasC) {
     auto scaledC = materializeScaledConstantTensor(c, gemmOpAdaptor.getBeta().convertToFloat(), rewriter, loc);
@@ -484,13 +514,7 @@ LogicalResult GemmToSpatialComputeBatch::matchAndRewrite(ONNXGemmOp gemmOp,
     auto cType = cast<RankedTensorType>(c.getType());
     if (cType.getRank() == 1) {
       auto expandedType = RankedTensorType::get({1, cType.getDimSize(0)}, cType.getElementType());
-      c = tensor::ExpandShapeOp::create(rewriter,
-                                        loc,
-                                        expandedType,
-                                        c,
-                                        SmallVector<ReassociationIndices> {
-                                          {0, 1}
-      });
+      c = expandRankOneBias(c, expandedType, rewriter, loc);
       cType = cast<RankedTensorType>(c.getType());
     }
     if (!cType.hasStaticShape()) {

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/MatMul.cpp

@@ -2,11 +2,11 @@
 #include "mlir/IR/BuiltinTypes.h"
 #include "mlir/IR/PatternMatch.h"
 
-#include "llvm/ADT/SmallPtrSet.h"
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -36,49 +36,27 @@ static Value extractBatchMatrix(Value value,
   SmallVector<OpFoldResult> sizes = {
     rewriter.getIndexAttr(1), rewriter.getIndexAttr(rows), rewriter.getIndexAttr(cols)};
   SmallVector<OpFoldResult> strides = {rewriter.getIndexAttr(1), rewriter.getIndexAttr(1), rewriter.getIndexAttr(1)};
-  Value slice = tensor::ExtractSliceOp::create(rewriter, loc, sliceType, value, offsets, sizes, strides);
-
   auto matrixType = RankedTensorType::get({rows, cols}, type.getElementType());
-  return tensor::CollapseShapeOp::create(rewriter,
-                                         loc,
-                                         matrixType,
-                                         slice,
-                                         SmallVector<ReassociationIndices> {
-                                           {0, 1},
-                                           {2}
-  });
-}
+  auto buildMatrix = [&](Value input) -> Value {
+    Value slice = tensor::ExtractSliceOp::create(rewriter, loc, sliceType, input, offsets, sizes, strides);
+    return tensor::CollapseShapeOp::create(rewriter,
+                                           loc,
+                                           matrixType,
+                                           slice,
+                                           SmallVector<ReassociationIndices> {
+                                             {0, 1},
+                                             {2}
+    });
+  };
 
-static bool isConstantLikeOperand(Value value) {
-  llvm::SmallPtrSet<Operation*, 8> visited;
+  if (isHostFoldableValue(value))
+    return buildMatrix(value);
 
-  while (auto* definingOp = value.getDefiningOp()) {
-    if (!visited.insert(definingOp).second)
-      return false;
-    if (definingOp->hasTrait<OpTrait::ConstantLike>())
-      return true;
-
-    if (auto extractSliceOp = dyn_cast<tensor::ExtractSliceOp>(definingOp)) {
-      value = extractSliceOp.getSource();
-      continue;
-    }
-    if (auto expandShapeOp = dyn_cast<tensor::ExpandShapeOp>(definingOp)) {
-      value = expandShapeOp.getSrc();
-      continue;
-    }
-    if (auto collapseShapeOp = dyn_cast<tensor::CollapseShapeOp>(definingOp)) {
-      value = collapseShapeOp.getSrc();
-      continue;
-    }
-    if (auto transposeOp = dyn_cast<ONNXTransposeOp>(definingOp)) {
-      value = transposeOp.getData();
-      continue;
-    }
-
-    return false;
-  }
-
-  return false;
+  auto batchMatrixCompute =
+    createSpatCompute<1>(rewriter, loc, TypeRange {matrixType}, {}, ValueRange {value}, [&](Value input) {
+      spatial::SpatYieldOp::create(rewriter, loc, buildMatrix(input));
+    });
+  return batchMatrixCompute.getResult(0);
 }
 
 static Value transposeLastTwoDims(Value value, PatternRewriter& rewriter, Location loc) {
@@ -107,15 +85,31 @@ static Value transposeLastTwoDimsInCompute(Value value, PatternRewriter& rewrite
     perm = {0, 2, 1};
   }
 
-  auto transposeCompute =
-    createSpatCompute<1>(rewriter, loc, transposedType, {}, ValueRange {value}, [&](Value input) {
-      Value transposed =
-        ONNXTransposeOp::create(rewriter, loc, transposedType, input, rewriter.getI64ArrayAttr(perm));
-      spatial::SpatYieldOp::create(rewriter, loc, transposed);
-    });
+  auto transposeCompute = createSpatCompute<1>(rewriter, loc, transposedType, {}, ValueRange {value}, [&](Value input) {
+    Value transposed = ONNXTransposeOp::create(rewriter, loc, transposedType, input, rewriter.getI64ArrayAttr(perm));
+    spatial::SpatYieldOp::create(rewriter, loc, transposed);
+  });
   return transposeCompute.getResult(0);
 }
 
+static Value concatValues(ValueRange inputs, int64_t axis, PatternRewriter& rewriter, Location loc) {
+  auto firstType = cast<RankedTensorType>(inputs.front().getType());
+  SmallVector<int64_t> outputShape(firstType.getShape().begin(), firstType.getShape().end());
+  int64_t concatDimSize = 0;
+  for (Value input : inputs)
+    concatDimSize += cast<RankedTensorType>(input.getType()).getDimSize(axis);
+  outputShape[axis] = concatDimSize;
+  auto resultType = RankedTensorType::get(outputShape, firstType.getElementType(), firstType.getEncoding());
+
+  if (llvm::all_of(inputs, isHostFoldableValue))
+    return createSpatConcat(rewriter, loc, axis, inputs);
+
+  auto concatCompute = createSpatCompute(rewriter, loc, TypeRange {resultType}, {}, inputs, [&](ValueRange args) {
+    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, axis, args));
+  });
+  return concatCompute.getResult(0);
+}
+
 struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
   using OpRewritePattern::OpRewritePattern;
 
@@ -157,7 +151,7 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
     }
 
     Location loc = matmulOp.getLoc();
-    bool useTransposedForm = isConstantLikeOperand(matmulOp.getA()) && !isConstantLikeOperand(matmulOp.getB());
+    bool useTransposedForm = isHostFoldableValue(matmulOp.getA()) && !isHostFoldableValue(matmulOp.getB());
 
     Value lhs = matmulOp.getA();
     Value rhs = matmulOp.getB();
@@ -193,8 +187,14 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
                                             rewriter.getBoolAttr(false),
                                             rewriter.getBoolAttr(false))
                            .getY();
-      if (useTransposedForm)
-        gemmResult = ONNXTransposeOp::create(rewriter, loc, outType, gemmResult, rewriter.getI64ArrayAttr({1, 0}));
+      if (useTransposedForm) {
+        auto transposeCompute =
+          createSpatCompute<1>(rewriter, loc, TypeRange {outType}, {}, gemmResult, [&](Value input) {
+            Value transposed = ONNXTransposeOp::create(rewriter, loc, outType, input, rewriter.getI64ArrayAttr({1, 0}));
+            spatial::SpatYieldOp::create(rewriter, loc, transposed);
+          });
+        gemmResult = transposeCompute.getResult(0);
+      }
       rewriter.replaceOp(matmulOp, gemmResult);
       return success();
     }
@@ -215,24 +215,30 @@ struct MatMulToGemm : OpRewritePattern<ONNXMatMulOp> {
                                             rewriter.getBoolAttr(false),
                                             rewriter.getBoolAttr(false))
                            .getY();
-      if (useTransposedForm)
-        gemmResult = ONNXTransposeOp::create(
-          rewriter,
-          loc,
-          RankedTensorType::get({m, n}, outType.getElementType()),
-          gemmResult,
-          rewriter.getI64ArrayAttr({1, 0}));
-      batchResults.push_back(tensor::ExpandShapeOp::create(rewriter,
-                                                           loc,
-                                                           batchedOutType,
-                                                           gemmResult,
-                                                           SmallVector<ReassociationIndices> {
-                                                             {0, 1},
-                                                             {2}
-      }));
+      auto batchResultCompute =
+        createSpatCompute<1>(rewriter, loc, TypeRange {batchedOutType}, {}, gemmResult, [&](Value input) {
+          Value resultMatrix = input;
+          if (useTransposedForm) {
+            resultMatrix = ONNXTransposeOp::create(rewriter,
+                                                   loc,
+                                                   RankedTensorType::get({m, n}, outType.getElementType()),
+                                                   input,
+                                                   rewriter.getI64ArrayAttr({1, 0}));
+          }
+          Value expanded = tensor::ExpandShapeOp::create(rewriter,
+                                                         loc,
+                                                         batchedOutType,
+                                                         resultMatrix,
+                                                         SmallVector<ReassociationIndices> {
+                                                           {0, 1},
+                                                           {2}
+          });
+          spatial::SpatYieldOp::create(rewriter, loc, expanded);
+        });
+      batchResults.push_back(batchResultCompute.getResult(0));
     }
 
-    Value result = createSpatConcat(rewriter, loc, /*axis=*/0, batchResults);
+    Value result = concatValues(batchResults, /*axis=*/0, rewriter, loc);
     rewriter.replaceOp(matmulOp, result);
     return success();
   }

src/PIM/Conversion/ONNXToSpatial/Patterns/Math/ReduceMean.cpp

@@ -6,7 +6,8 @@
 #include <algorithm>
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -81,6 +82,24 @@ createAverageCompute(Value input, RankedTensorType resultType, ConversionPattern
   return computeOp.getResult(0);
 }
 
+static Value concatValues(ValueRange inputs, int64_t axis, ConversionPatternRewriter& rewriter, Location loc) {
+  auto firstType = cast<RankedTensorType>(inputs.front().getType());
+  SmallVector<int64_t> outputShape(firstType.getShape().begin(), firstType.getShape().end());
+  int64_t concatDimSize = 0;
+  for (Value input : inputs)
+    concatDimSize += cast<RankedTensorType>(input.getType()).getDimSize(axis);
+  outputShape[axis] = concatDimSize;
+  auto resultType = RankedTensorType::get(outputShape, firstType.getElementType(), firstType.getEncoding());
+
+  if (llvm::all_of(inputs, isHostFoldableValue))
+    return createSpatConcat(rewriter, loc, axis, inputs);
+
+  auto concatCompute = createSpatCompute(rewriter, loc, TypeRange {resultType}, {}, inputs, [&](ValueRange args) {
+    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, axis, args));
+  });
+  return concatCompute.getResult(0);
+}
+
 static Value buildReduceMeanKeepdims(Value input,
                                      ArrayRef<bool> reducedAxes,
                                      int64_t axis,
@@ -100,7 +119,7 @@ static Value buildReduceMeanKeepdims(Value input,
   for (Value slice : slices)
     reducedSlices.push_back(buildReduceMeanKeepdims(slice, reducedAxes, axis + 1, leafType, rewriter, loc));
 
-  return createSpatConcat(rewriter, loc, axis, reducedSlices);
+  return concatValues(reducedSlices, axis, rewriter, loc);
 }
 
 static Value squeezeReducedAxes(Value keepdimsValue,
@@ -115,9 +134,16 @@ static Value squeezeReducedAxes(Value keepdimsValue,
     return tensor::FromElementsOp::create(rewriter, loc, resultType, ValueRange {element});
   }
 
-  return tensor::CollapseShapeOp::create(
-           rewriter, loc, resultType, keepdimsValue, buildCollapseReassociation(reducedAxes))
-    .getResult();
+  auto reassociation = buildCollapseReassociation(reducedAxes);
+  if (isHostFoldableValue(keepdimsValue))
+    return tensor::CollapseShapeOp::create(rewriter, loc, resultType, keepdimsValue, reassociation).getResult();
+
+  auto squeezeCompute =
+    createSpatCompute<1>(rewriter, loc, TypeRange {resultType}, {}, ValueRange {keepdimsValue}, [&](Value input) {
+      Value collapsed = tensor::CollapseShapeOp::create(rewriter, loc, resultType, input, reassociation);
+      spatial::SpatYieldOp::create(rewriter, loc, collapsed);
+    });
+  return squeezeCompute.getResult(0);
 }
 
 struct ReduceMeanToSpatialCompute : OpConversionPattern<ONNXReduceMeanV13Op> {

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Pool.cpp

@@ -31,8 +31,8 @@ static int64_t getOptionalI64(std::optional<ArrayAttrT> arrayAttr, size_t index,
 }
 
 template <typename PoolOp>
-static FailureOr<Value>
-concatAlongAxis(ConversionPatternRewriter& rewriter, Location loc, PoolOp poolOp, int64_t axis, ArrayRef<Value> values) {
+static FailureOr<Value> concatAlongAxis(
+  ConversionPatternRewriter& rewriter, Location loc, PoolOp poolOp, int64_t axis, ArrayRef<Value> values) {
   if (values.empty()) {
     poolOp.emitOpError("failed to build pooled output because an intermediate concatenation input list was empty");
     return failure();
@@ -68,8 +68,8 @@ reduceWindowValues(ConversionPatternRewriter& rewriter, Location loc, Operation*
   return reduced;
 }
 
-static FailureOr<Value>
-scaleAverageWindow(ConversionPatternRewriter& rewriter, Location loc, Operation* op, Value reducedWindow, int64_t divisor) {
+static FailureOr<Value> scaleAverageWindow(
+  ConversionPatternRewriter& rewriter, Location loc, Operation* op, Value reducedWindow, int64_t divisor) {
   if (divisor <= 0) {
     op->emitOpError("AveragePool divisor must be positive");
     return failure();

src/PIM/Conversion/ONNXToSpatial/Patterns/NN/Softmax.cpp

@@ -2,7 +2,8 @@
 #include "mlir/Transforms/DialectConversion.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -32,6 +33,24 @@ static Value createSoftmaxCompute(Value input, ConversionPatternRewriter& rewrit
   return computeOp.getResult(0);
 }
 
+static Value concatValues(ValueRange inputs, int64_t axis, ConversionPatternRewriter& rewriter, Location loc) {
+  auto firstType = cast<RankedTensorType>(inputs.front().getType());
+  SmallVector<int64_t> outputShape(firstType.getShape().begin(), firstType.getShape().end());
+  int64_t concatDimSize = 0;
+  for (Value input : inputs)
+    concatDimSize += cast<RankedTensorType>(input.getType()).getDimSize(axis);
+  outputShape[axis] = concatDimSize;
+  auto resultType = RankedTensorType::get(outputShape, firstType.getElementType(), firstType.getEncoding());
+
+  if (llvm::all_of(inputs, isHostFoldableValue))
+    return createSpatConcat(rewriter, loc, axis, inputs);
+
+  auto concatCompute = createSpatCompute(rewriter, loc, TypeRange {resultType}, {}, inputs, [&](ValueRange args) {
+    spatial::SpatYieldOp::create(rewriter, loc, createSpatConcat(rewriter, loc, axis, args));
+  });
+  return concatCompute.getResult(0);
+}
+
 static Value
 buildSoftmax(Value input, int64_t softmaxAxis, int64_t axis, ConversionPatternRewriter& rewriter, Location loc) {
   auto inputType = cast<RankedTensorType>(input.getType());
@@ -47,7 +66,7 @@ buildSoftmax(Value input, int64_t softmaxAxis, int64_t axis, ConversionPatternRe
   for (Value slice : slices)
     rebuiltSlices.push_back(buildSoftmax(slice, softmaxAxis, axis + 1, rewriter, loc));
 
-  return createSpatConcat(rewriter, loc, axis, rebuiltSlices);
+  return concatValues(rebuiltSlices, axis, rewriter, loc);
 }
 
 struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
@@ -92,8 +111,13 @@ struct SoftmaxToSpatialCompute : OpConversionPattern<ONNXSoftmaxOp> {
       Value transposedInput = preTransposeCompute.getResult(0);
       Value transposedResult = buildSoftmax(
         transposedInput, /*softmaxAxis=*/inputType.getRank() - 1, /*axis=*/0, rewriter, softmaxOp.getLoc());
-      result = ONNXTransposeOp::create(
-        rewriter, softmaxOp.getLoc(), inputType, transposedResult, rewriter.getI64ArrayAttr(inversePermutation));
+      auto postTransposeCompute =
+        createSpatCompute<1>(rewriter, softmaxOp.getLoc(), TypeRange {inputType}, {}, transposedResult, [&](Value x) {
+          Value transposed = ONNXTransposeOp::create(
+            rewriter, softmaxOp.getLoc(), inputType, x, rewriter.getI64ArrayAttr(inversePermutation));
+          spatial::SpatYieldOp::create(rewriter, softmaxOp.getLoc(), transposed);
+        });
+      result = postTransposeCompute.getResult(0);
     }
 
     rewriter.replaceOp(softmaxOp, result);

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Concat.cpp

@@ -2,6 +2,8 @@
 #include "mlir/IR/PatternMatch.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/ComputeRegionBuilder.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
@@ -18,7 +20,17 @@ struct Concat : public OpConversionPattern<ONNXConcatOp> {
     auto inputs = adaptor.getInputs();
     int64_t axis = adaptor.getAxis();
 
-    rewriter.replaceOp(maxpoolOp, createSpatConcat(rewriter, maxpoolOp.getLoc(), axis, inputs));
+    if (llvm::all_of(inputs, isHostFoldableValue)) {
+      rewriter.replaceOp(maxpoolOp, createSpatConcat(rewriter, maxpoolOp.getLoc(), axis, inputs));
+      return success();
+    }
+
+    auto computeOp = createSpatCompute(
+      rewriter, maxpoolOp.getLoc(), TypeRange {maxpoolOp.getResult().getType()}, {}, inputs, [&](ValueRange args) {
+        spatial::SpatYieldOp::create(
+          rewriter, maxpoolOp.getLoc(), createSpatConcat(rewriter, maxpoolOp.getLoc(), axis, args));
+      });
+    rewriter.replaceOp(maxpoolOp, computeOp.getResults());
 
     return success();
   }

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Gather.cpp

@@ -6,7 +6,7 @@
 #include "llvm/ADT/SmallVector.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Reshape.cpp

@@ -3,7 +3,10 @@
 
 #include "llvm/ADT/SmallVector.h"
 
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
 using namespace mlir;
@@ -95,18 +98,33 @@ struct Reshape : OpConversionPattern<ONNXReshapeOp> {
       return success();
     }
 
+    auto replaceWithReshape = [&](auto buildReshape) -> LogicalResult {
+      if (isHostFoldableValue(adaptor.getData())) {
+        rewriter.replaceOp(reshapeOp, buildReshape(adaptor.getData()));
+        return success();
+      }
+
+      auto computeOp = createSpatCompute<1>(
+        rewriter, reshapeOp.getLoc(), TypeRange {resultType}, {}, adaptor.getData(), [&](Value data) {
+          Value reshaped = buildReshape(data);
+          spatial::SpatYieldOp::create(rewriter, reshapeOp.getLoc(), reshaped);
+        });
+      rewriter.replaceOp(reshapeOp, computeOp.getResults());
+      return success();
+    };
+
     SmallVector<ReassociationIndices> reassociation;
     if (sourceType.getRank() > resultType.getRank()
-        && inferCollapseReassociation(sourceType.getShape(), resultType.getShape(), reassociation)) {
-      rewriter.replaceOpWithNewOp<tensor::CollapseShapeOp>(reshapeOp, resultType, adaptor.getData(), reassociation);
-      return success();
-    }
+        && inferCollapseReassociation(sourceType.getShape(), resultType.getShape(), reassociation))
+      return replaceWithReshape([&](Value data) {
+        return tensor::CollapseShapeOp::create(rewriter, reshapeOp.getLoc(), resultType, data, reassociation);
+      });
 
     if (sourceType.getRank() < resultType.getRank()
-        && inferExpandReassociation(sourceType.getShape(), resultType.getShape(), reassociation)) {
-      rewriter.replaceOpWithNewOp<tensor::ExpandShapeOp>(reshapeOp, resultType, adaptor.getData(), reassociation);
-      return success();
-    }
+        && inferExpandReassociation(sourceType.getShape(), resultType.getShape(), reassociation))
+      return replaceWithReshape([&](Value data) {
+        return tensor::ExpandShapeOp::create(rewriter, reshapeOp.getLoc(), resultType, data, reassociation);
+      });
 
     return failure();
   }

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Resize.cpp

@@ -6,7 +6,7 @@
 #include <algorithm>
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
 #include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 

src/PIM/Conversion/ONNXToSpatial/Patterns/Tensor/Split.cpp

@@ -2,7 +2,9 @@
 #include "mlir/Transforms/DialectConversion.h"
 
 #include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
-#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Patterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/HostFoldability.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
 #include "src/Dialect/ONNX/ONNXOps.hpp"
 
 using namespace mlir;
@@ -47,16 +49,40 @@ struct Split : OpConversionPattern<ONNXSplitOp> {
     outputs.reserve(splitOp.getNumResults());
 
     int64_t offset = 0;
+    SmallVector<RankedTensorType> resultTypes;
+    resultTypes.reserve(splitOp.getNumResults());
+    SmallVector<int64_t> sliceSizes;
+    sliceSizes.reserve(splitOp.getNumResults());
     for (Value result : splitOp.getResults()) {
       auto resultType = dyn_cast<RankedTensorType>(result.getType());
       if (!resultType || !resultType.hasStaticShape())
         return failure();
-      int64_t sliceSize = resultType.getShape()[axis];
-      outputs.push_back(extractSliceAt(adaptor.getInput(), axis, offset, sliceSize, rewriter, splitOp.getLoc()));
-      offset += sliceSize;
+      resultTypes.push_back(resultType);
+      sliceSizes.push_back(resultType.getShape()[axis]);
     }
 
-    rewriter.replaceOp(splitOp, outputs);
+    if (isHostFoldableValue(adaptor.getInput())) {
+      for (int64_t sliceSize : sliceSizes) {
+        outputs.push_back(extractSliceAt(adaptor.getInput(), axis, offset, sliceSize, rewriter, splitOp.getLoc()));
+        offset += sliceSize;
+      }
+      rewriter.replaceOp(splitOp, outputs);
+      return success();
+    }
+
+    auto computeOp = createSpatCompute<1>(
+      rewriter, splitOp.getLoc(), TypeRange(splitOp.getResultTypes()), {}, adaptor.getInput(), [&](Value input) {
+        SmallVector<Value> runtimeOutputs;
+        runtimeOutputs.reserve(resultTypes.size());
+        int64_t runtimeOffset = 0;
+        for (int64_t sliceSize : sliceSizes) {
+          runtimeOutputs.push_back(extractSliceAt(input, axis, runtimeOffset, sliceSize, rewriter, splitOp.getLoc()));
+          runtimeOffset += sliceSize;
+        }
+        spatial::SpatYieldOp::create(rewriter, splitOp.getLoc(), runtimeOutputs);
+      });
+
+    rewriter.replaceOp(splitOp, computeOp.getResults());
     return success();
   }
 };

src/PIM/Conversion/ONNXToSpatial/PostPatterns.cpp

@@ -0,0 +1,265 @@
+#include "mlir/Dialect/Arith/IR/Arith.h"
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/Dialect/Tensor/IR/Tensor.h"
+#include "mlir/IR/IRMapping.h"
+#include "mlir/IR/PatternMatch.h"
+
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/ADT/SmallVector.h"
+
+#include "src/Accelerators/PIM/Common/IR/WeightUtils.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/WeightMaterialization.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PostPatterns.hpp"
+#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
+#include "src/Dialect/ONNX/ONNXOps.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+static bool isWeightMaterializationHelperUser(Operation* op) {
+  return isa<tensor::ExtractSliceOp, tensor::ExpandShapeOp, tensor::CollapseShapeOp, ONNXTransposeOp>(op);
+}
+
+static bool canPromoteInputBlockArgument(BlockArgument arg) {
+  return !arg.use_empty() && llvm::all_of(arg.getUsers(), isWeightMaterializationHelperUser);
+}
+
+static bool isDirectConstantValue(Value value) {
+  return isa_and_nonnull<arith::ConstantOp, ONNXConstantOp>(value.getDefiningOp());
+}
+
+// Collapses one-lane batches so later phases do not carry batch-only structure unnecessarily.
+struct FoldSingleLaneComputeBatchPattern : OpRewritePattern<spatial::SpatComputeBatch> {
+  using OpRewritePattern<spatial::SpatComputeBatch>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(spatial::SpatComputeBatch batchOp, PatternRewriter& rewriter) const override {
+    if (batchOp.getLaneCount() != 1)
+      return rewriter.notifyMatchFailure(batchOp, "requires a single lane");
+
+    auto loc = batchOp.getLoc();
+    rewriter.setInsertionPoint(batchOp);
+    auto computeOp =
+      spatial::SpatCompute::create(rewriter, loc, batchOp.getResultTypes(), batchOp.getWeights(), batchOp.getInputs());
+    computeOp.getProperties().setOperandSegmentSizes(
+      {static_cast<int>(batchOp.getWeights().size()), static_cast<int>(batchOp.getInputs().size())});
+
+    Block& templateBlock = batchOp.getBody().front();
+    SmallVector<Type> blockArgTypes;
+    SmallVector<Location> blockArgLocs;
+    blockArgTypes.reserve(templateBlock.getNumArguments());
+    blockArgLocs.reserve(templateBlock.getNumArguments());
+    for (BlockArgument arg : templateBlock.getArguments()) {
+      blockArgTypes.push_back(arg.getType());
+      blockArgLocs.push_back(loc);
+    }
+
+    auto* newBlock =
+      rewriter.createBlock(&computeOp.getBody(), computeOp.getBody().end(), TypeRange(blockArgTypes), blockArgLocs);
+    IRMapping mapper;
+    for (auto [oldArg, newArg] : llvm::zip(templateBlock.getArguments(), newBlock->getArguments()))
+      mapper.map(oldArg, newArg);
+
+    rewriter.setInsertionPointToEnd(newBlock);
+    for (Operation& op : templateBlock)
+      rewriter.clone(op, mapper);
+
+    batchOp->replaceAllUsesWith(computeOp->getResults());
+    rewriter.eraseOp(batchOp);
+    return success();
+  }
+};
+
+// Promotes foldable helper chains from runtime inputs to weights to avoid artificial compute inputs.
+struct PromoteWeightLikeComputeInputsPattern : OpRewritePattern<spatial::SpatCompute> {
+  using OpRewritePattern<spatial::SpatCompute>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(spatial::SpatCompute compute, PatternRewriter& rewriter) const override {
+    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
+    bool needsRewrite = false;
+    Block& oldBlock = compute.getBody().front();
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (inputIdx >= oldBlock.getNumArguments())
+        continue;
+      if (!isWeightLikeComputeOperand(input))
+        continue;
+      if (isDirectConstantValue(input) && !canPromoteInputBlockArgument(oldBlock.getArgument(inputIdx)))
+        continue;
+      promoteInput[inputIdx] = true;
+      needsRewrite = true;
+    }
+    if (!needsRewrite)
+      return rewriter.notifyMatchFailure(compute, "no weight-like inputs to promote");
+
+    rewriter.setInsertionPointAfter(compute);
+
+    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
+    SmallVector<Value> newInputs;
+    SmallVector<Type> newInputTypes;
+    SmallVector<Location> newInputLocs;
+    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
+    newInputs.reserve(compute.getInputs().size());
+    newInputTypes.reserve(compute.getInputs().size());
+    newInputLocs.reserve(compute.getInputs().size());
+
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (promoteInput[inputIdx]) {
+        newWeights.push_back(input);
+        continue;
+      }
+      newInputs.push_back(input);
+      newInputTypes.push_back(input.getType());
+      newInputLocs.push_back(input.getLoc());
+    }
+
+    auto newCompute =
+      spatial::SpatCompute::create(rewriter, compute.getLoc(), compute.getResultTypes(), newWeights, newInputs);
+    auto* newBlock =
+      rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), newInputTypes, newInputLocs);
+    newCompute.getProperties().setOperandSegmentSizes(
+      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
+    rewriter.setInsertionPointToStart(newBlock);
+
+    IRRewriter bodyRewriter(rewriter.getContext());
+    bodyRewriter.setInsertionPointToStart(newBlock);
+
+    IRMapping mapper;
+    size_t newInputIdx = 0;
+    for (auto [oldInputIdx, oldArg] : llvm::enumerate(oldBlock.getArguments())) {
+      if (!promoteInput[oldInputIdx]) {
+        mapper.map(oldArg, newBlock->getArgument(newInputIdx++));
+        continue;
+      }
+
+      auto clonedValue = materializeWeightLikeValueInBlock(compute.getInputs()[oldInputIdx], bodyRewriter, mapper);
+      if (failed(clonedValue))
+        return rewriter.notifyMatchFailure(compute, "failed to materialize promoted weight-like operand");
+      mapper.map(oldArg, *clonedValue);
+    }
+
+    for (Operation& op : oldBlock.without_terminator())
+      rewriter.clone(op, mapper);
+
+    auto oldYield = cast<spatial::SpatYieldOp>(oldBlock.getTerminator());
+    SmallVector<Value> newYieldOperands;
+    newYieldOperands.reserve(oldYield.getOutputs().size());
+    for (Value operand : oldYield.getOutputs()) {
+      auto mapped = mapper.lookupOrNull(operand);
+      newYieldOperands.push_back(mapped ? cast<Value>(mapped) : operand);
+    }
+    spatial::SpatYieldOp::create(rewriter, oldYield.getLoc(), newYieldOperands);
+
+    rewriter.replaceOp(compute, newCompute.getResults());
+    return success();
+  }
+};
+
+// Promotes foldable batch helper chains to weights while preserving compact compute_batch IR.
+struct PromoteWeightLikeComputeBatchInputsPattern : OpRewritePattern<spatial::SpatComputeBatch> {
+  using OpRewritePattern<spatial::SpatComputeBatch>::OpRewritePattern;
+
+  LogicalResult matchAndRewrite(spatial::SpatComputeBatch compute, PatternRewriter& rewriter) const override {
+    SmallVector<bool> promoteInput(compute.getInputs().size(), false);
+    bool needsRewrite = false;
+    Block& oldBlock = compute.getBody().front();
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (inputIdx >= oldBlock.getNumArguments())
+        continue;
+      if (!isWeightLikeComputeOperand(input))
+        continue;
+      if (isDirectConstantValue(input) && !canPromoteInputBlockArgument(oldBlock.getArgument(inputIdx)))
+        continue;
+      promoteInput[inputIdx] = true;
+      needsRewrite = true;
+    }
+    if (!needsRewrite)
+      return rewriter.notifyMatchFailure(compute, "no weight-like batch inputs to promote");
+
+    rewriter.setInsertionPointAfter(compute);
+
+    SmallVector<Value> newWeights(compute.getWeights().begin(), compute.getWeights().end());
+    SmallVector<Value> newInputs;
+    SmallVector<Type> newInputTypes;
+    SmallVector<Location> newInputLocs;
+    newWeights.reserve(compute.getWeights().size() + compute.getInputs().size());
+    newInputs.reserve(compute.getInputs().size());
+    newInputTypes.reserve(compute.getInputs().size());
+    newInputLocs.reserve(compute.getInputs().size());
+
+    for (auto [inputIdx, input] : llvm::enumerate(compute.getInputs())) {
+      if (promoteInput[inputIdx]) {
+        newWeights.push_back(input);
+        continue;
+      }
+      newInputs.push_back(input);
+      newInputTypes.push_back(input.getType());
+      newInputLocs.push_back(input.getLoc());
+    }
+
+    auto newCompute =
+      spatial::SpatComputeBatch::create(rewriter,
+                                        compute.getLoc(),
+                                        compute.getResultTypes(),
+                                        rewriter.getI32IntegerAttr(static_cast<int32_t>(compute.getLaneCount())),
+                                        newWeights,
+                                        newInputs);
+    auto* newBlock =
+      rewriter.createBlock(&newCompute.getBody(), newCompute.getBody().end(), newInputTypes, newInputLocs);
+    newCompute.getProperties().setOperandSegmentSizes(
+      {static_cast<int>(newWeights.size()), static_cast<int>(newInputs.size())});
+    rewriter.setInsertionPointToStart(newBlock);
+
+    IRRewriter bodyRewriter(rewriter.getContext());
+    bodyRewriter.setInsertionPointToStart(newBlock);
+
+    IRMapping mapper;
+    size_t newInputIdx = 0;
+    for (auto [oldInputIdx, oldArg] : llvm::enumerate(oldBlock.getArguments())) {
+      if (!promoteInput[oldInputIdx]) {
+        mapper.map(oldArg, newBlock->getArgument(newInputIdx++));
+        continue;
+      }
+
+      auto clonedValue = materializeWeightLikeValueInBlock(compute.getInputs()[oldInputIdx], bodyRewriter, mapper);
+      if (failed(clonedValue))
+        return rewriter.notifyMatchFailure(compute, "failed to materialize promoted batch weight-like operand");
+      mapper.map(oldArg, *clonedValue);
+    }
+
+    for (Operation& op : oldBlock.without_terminator())
+      rewriter.clone(op, mapper);
+
+    auto oldYield = cast<spatial::SpatYieldOp>(oldBlock.getTerminator());
+    SmallVector<Value> newYieldOperands;
+    newYieldOperands.reserve(oldYield.getOutputs().size());
+    for (Value operand : oldYield.getOutputs()) {
+      auto mapped = mapper.lookupOrNull(operand);
+      newYieldOperands.push_back(mapped ? cast<Value>(mapped) : operand);
+    }
+    spatial::SpatYieldOp::create(rewriter, oldYield.getLoc(), newYieldOperands);
+
+    rewriter.replaceOp(compute, newCompute.getResults());
+    return success();
+  }
+};
+
+} // namespace
+
+void populateEarlyPostPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
+  patterns.add<FoldSingleLaneComputeBatchPattern>(ctx);
+}
+
+void populatePostPatterns(RewritePatternSet& patterns, MLIRContext* ctx) {
+  patterns.add<PromoteWeightLikeComputeInputsPattern, PromoteWeightLikeComputeBatchInputsPattern>(ctx);
+}
+
+void annotateWeightsConstants(func::FuncOp funcOp) {
+  funcOp.walk([&](arith::ConstantOp constantOp) {
+    if (hasOnlySpatialMvmVmmWeightUses(constantOp.getResult()))
+      markWeightAlways(constantOp);
+  });
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/PostPatterns.hpp

@@ -0,0 +1,14 @@
+#pragma once
+
+#include "mlir/Dialect/Func/IR/FuncOps.h"
+#include "mlir/IR/MLIRContext.h"
+
+namespace onnx_mlir {
+
+void populateEarlyPostPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
+
+void populatePostPatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
+
+void annotateWeightsConstants(mlir::func::FuncOp funcOp);
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/PrePatterns.cpp

@@ -0,0 +1,25 @@
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/Common/Common.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ConversionPatterns.hpp"
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/PrePatterns.hpp"
+
+using namespace mlir;
+
+namespace onnx_mlir {
+
+namespace {
+
+#include "src/Accelerators/PIM/Conversion/ONNXToSpatial/ONNXToSpatial.hpp.inc"
+
+} // namespace
+
+void populatePrePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx) {
+  patterns.add<onnxToArithConstant>(ctx);
+  patterns.add<convAddToConvWithBiasLeft>(ctx);
+  patterns.add<convAddToConvWithBiasRight>(ctx);
+  patterns.add<matMulAddToGemm>(ctx);
+  patterns.add<matMulToGemm>(ctx);
+  patterns.add<removeFlattenSameShape>(ctx);
+  populateMatMulRewritePatterns(patterns, ctx);
+}
+
+} // namespace onnx_mlir

src/PIM/Conversion/ONNXToSpatial/PrePatterns.hpp

@@ -0,0 +1,10 @@
+#pragma once
+
+#include "mlir/IR/MLIRContext.h"
+#include "mlir/Transforms/DialectConversion.h"
+
+namespace onnx_mlir {
+
+void populatePrePatterns(mlir::RewritePatternSet& patterns, mlir::MLIRContext* ctx);
+
+} // namespace onnx_mlir